Cooperative Adaptive Cruise Control: Driver Acceptance of Following Gap Settings Less Than One Second

Nowakowski, Christopher; O’Connell, Jessica; Shladover, Steven E; Cody, Delphine

doi:10.1037/e578852012-003

Cited by 71 publications

(70 citation statements)

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“…For the CACC system, the shortest gap is set at 0.6 seconds. This value has been chosen based on estimates of the separation needed to enable crash avoidance under emergency conditions, as well as previous tests of acceptance by drivers from the general public [24]. The other two available CACC gap settings are 0.9 and 1.1 seconds.…”

Section: Control Designmentioning

confidence: 99%

“…In previous PATH research, a CACC involving two vehicles was tested with very favorable results [23], [24], [6]. Building on that previous work, this paper describes a new control system design and implementation that is integrated in four production vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…A constant time-gap car following strategy was implemented similar to the commercial ACC, but with the availability of significantly shorter time-gap settings. This is achievable because V2V communications permit tighter control of vehicle spacing and guarantee string stability, so that inter-vehicle time-gap settings significantly shorter than the production ACC time-gap settings are comfortable and acceptable to drivers [24]. The whole system was then tested in real traffic scenarios in order to compare its performance to the production ACC system installed in the vehicles.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cooperative Adaptive Cruise Control in Real Traffic Situations

Milanés

Shladover

Spring

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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938

429

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Abstract-Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents, but also to improving traffic flow. Adaptive Cruise Control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to Cooperative ACC (CACC). This paper presents the design, development, implementation and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

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Section: Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Adaptive Cruise Control in Real Traffic Situations

Milanés

Shladover

Spring

et al. 2014

IEEE Trans. Intell. Transport. Syst.

Self Cite

938

429

View full text Add to dashboard Cite

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“…The control design should yield technically feasible desired acceleration signals and preferably, its performance should meet additional operational specifications: resilient to fluctuations in velocity and spacing errors caused by maneuvers of the leading vehicle like cutting in, cutting out, braking and accelerating; yield continuous desired acceleration signals and operate with a time gap range of (not restricted to) 0.8-2.2 s, which is typically observed in human drivers [17]; it should be locally stable and analyzed for string stability properties to understand the potential impacts on traffic flow. …”

Section: A Design Specificationsmentioning

confidence: 99%

Design and analysis of Full Range Adaptive Cruise Control with integrated collision a voidance strategy

Mullakkal-Babu

Wang

Arem

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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“…Fusion of data is mandatory for overcoming the errors from different sensors [18]. These sensors also include the wheel sensors, yaw rate sensors and other motion sensors that are vital for a vehicle's movement [19].…”

Section: Overall On-board Data Flow Diagram For CCCmentioning

confidence: 99%

A threat based approach to computational offloading for collaborative cruise control

Sheik

Maple

Watson

et al. 2017

Proceedings of the Second International Conference on Internet of Things, Data and Cloud Computing

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Copies of full items can be used for personal research or study, educational, or not-for profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. ABSTRACTThe interaction between discrete components of Internet of Things (IoT) and Intelligent Transportation Systems (ITS) is vital for a collaborative system. The secure and reliable use of Cruise Control (CC) with Cloud and Edge Cloud to achieve complete autonomy for a vehicle is a key component and a major challenge for ITS. This research unravels the complications that arise when Adaptive Cruise Control (ACC) is incorporated into a collaborative environment. It mainly answers the question of where to securely compute Collaborative Cruise Control's (CCC) data in a connected environment. To address this, the paper initially reviews previous research in the domain of Vehicular Cloud, ITS architecture, related threat modelling approaches, and secure implementations of ACC. An overview application model for CCC is developed for performing a threat analysis with the purpose of investigating the reasons why a vehicle suffers collision. Through the use of interviews, the research analyses and suggests the location of computational data by creating a taxonomy between the Edge Cloud, Cloud and the On-board Unit (OBU) while validating the model.

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Cooperative Adaptive Cruise Control: Driver Acceptance of Following Gap Settings Less Than One Second

Cited by 71 publications

References 0 publications

Cooperative Adaptive Cruise Control in Real Traffic Situations

Cooperative Adaptive Cruise Control in Real Traffic Situations

Design and analysis of Full Range Adaptive Cruise Control with integrated collision a voidance strategy

A threat based approach to computational offloading for collaborative cruise control

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